Developing a new method to identify the fraction of green vegetation from satellite data at high spatial resolution.

Both the type and composition of landforms affect atmospheric properties and clouds in a state-of-the-art Earth system model.

New research makes molecular-level calculations of water faster and easier.

Anthropogenically forced evolution of the leading neutral mode explains historical trends in global surface temperature, lending confidence to regional projections related to modes.

Aerosols from sea-spray that are transported by atmospheric rivers enhance snow precipitation in the Sierra Nevada Mountains.

The initial conditions and setup influence how large-eddy simulations representation atmospheric properties important for larger-scale models.

Changes in land use and land cover have diverse and contrasting effects on different types of rain.

Metrics and diagnostics that improve connections between model errors and their sources can lead to better precipitation modeling.

A circumglobal circulation pattern related to natural climate variability influences the location of summertime hot spots.

Increasing amounts of atmospheric carbon dioxide change when in the day tropical forest rainfall occurs.

The Earth system model aerosol-cloud diagnostics package version 1 uses aircraft, ship, and surface measurements to evaluate simulated aerosols in an Earth system model.

MSD-LIVE, a cloud-based data and code management system, will be released in summer 2022 for hundreds of scientists.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

Assessing a complex concert of the weather and terrain conditions that affect the growth of cumulus clouds into deep thunderstorms along mountain ranges.

Tropical cyclones induce sea surface cooling, but their associated heavy rainfall reduces the effect through increased ocean mixing.

Relaxing the simplifying assumptions in model energy budgets can affect their simulated regional water cycles.

New research from PNNL results in faster and more accurate variational quantum algorithm training.

New research quantifies the discrepancies between ground measurements of soil respiration and satellite estimates of photosynthesis.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.